Conventional computing systems provide for the storage of, and access to, large quantities of data. However, it may be difficult or inconvenient for users to locate desired data, or otherwise to interact with such large datasets in a desired manner.
In order to facilitate user interactions with large datasets, graphical user interfaces have been developed which are designed to provide visualizations of the data. For example, such graphical user interfaces may provide diagrams, charts, dashboards, or other visualizations representing data elements of a dataset, or aggregations or other combinations thereof. In some cases, users may be provided with an ability to zoom in or zoom out with respect to selected data elements, and/or to group selected subsets of data elements for collective viewing thereof.
While such systems and related techniques may be capable of providing a satisfactory user experience in certain contexts, significant room for improvement exists in the realm of visualizing, and otherwise interacting with, very large datasets. Moreover, in conventional data visualizations, the data visualizations may rely on predefined queries and data paths, and generally provide, at best, merely a simplified grouping of data elements corresponding to the predefined queries or data paths. Consequently, in these and other contexts, a user's ability to view and access data in a desired, flexible manner, and with a minimum of wait time or other inconvenience, may be effectively reduced or prevented.